Deep Learning Chips

Deep learning is a machine-learning technique that teaches a computer what to do, which comes naturally to humans.

Deep learning is a key technology behind driverless cars, which enable them to recognize a stop sign or differentiate a pedestrian from a lamp post. Deep learning is getting a lot of attention lately and for good reason. It’s achieving results that were not possible before.

In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound. Deep learning models achieve state-of-the-art accuracy, sometimes exceeding human-level performance.

How does ‘Deep Learning’ attain such impressive results?

The answer lies in a single word:  Accuracy

Deep learning achieves recognition accuracy at higher levels than ever before. This helps consumer electronics meet user expectations, and it is crucial for safety-critical applications, such as driverless cars. Recent advances in deep learning have improved to the point where deep learning outperforms humans in some tasks, such as classifying objects in images.

Examples of Deep Learning at work:-

Deep learning applications are used in industries from automated driving to medical devices.

Automated Driving: Automotive researchers have been using deep learning to automatically detect objects, such as stop signs and traffic lights. In addition, deep learning is used to detect pedestrians, which helps decrease accidents.

Aerospace and Defense: Deep learning is used to identify objects in a wide range from satellites that locate areas of interest to safe or unsafe zones for troops.

Medical Research: Cancer researchers have been using deep learning to automatically detect cancer cells. Teams at University of California, Los Angeles (UCLA) built an advanced microscope that yields a high-dimensional data set used to train a deep learning application to accurately identify cancer cells.

Industrial Automation: Deep learning has been helping to improve worker safety around heavy machinery by automatically detecting when people or objects are within an unsafe distance of machines.

Electronics: Deep learning has been enabling automated hearing and speech translation; for example, home assistance devices that respond to your voice and know your preferences are powered by deep learning applications.

Among countless other applications, deep learning generates captions for YouTube videos, serves up appealing food photos on blogs, and answers iPhone users’ questions through Siri. And as data scientists and researchers tackle increasingly complex deep learning projects, this type of artificial intelligence will become only more entangled in our daily lives.

The future of ‘Deep Learning’

The world has come a long way since Google built its deep learning model to identify cats. Now, it has started to use automated machine learning tools to create neural network layers in less time, employ deep learning in the medical field, and match related images with human accuracy.

As data scientists get closer to building highly-accurate deep learning models that learn without supervision, deep learning will become faster and less labor-intensive. This implies that only bigger and better things are yet to come.

So, according to a study…

An upcoming report of Allied Market Research, titled, Deep Learning Chip Market by Application and Industry Vertical – Global Opportunity Analysis and Industry Forecast, 2017-2023, drafts that increase in volume of large complex data, growth in the portable electronic market, and rise in popularity of Internet of Things (IoT) drive the global deep learning chip market. However, high implementation cost and low accuracy restrain this market growth. Introduction of automated appliances in consumer electronics and automotive sectors is expected to provide a lucrative opportunity for market development.

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Deep learning is a subset of machine learning in Artificial Intelligence (AI), which has networks capable of unsupervised learning from data that is unstructured or unlabeled.

At present, North America dominates this market, followed by Europe. In 2016, the U.S. dominated the North American market.

Deep learning has evolved hand-in-hand with the digital era, which has brought about an explosion of data in all forms and from every region of the world. Deep learning is used across all industries for a number of different tasks. Commercial apps that use image recognition, open-source platforms with consumer recommendation apps, and medical research tools to explore the possibility of reusing drugs for new ailments are a few examples of deep learning incorporation.